RNA velocity of single cells

Gioele La Manno, Ruslan Soldatov, Amit Zeisel, Emelie Braun, Hannah Hochgerner, Viktor Petukhov, Katja Lidschreiber, Maria E. Kastriti, Peter Lönnerberg, Alessandro Furlan, Jean Fan, Lars E. Borm, Zehua Liu, David Bruggen, Jimin Guo, Xiaoling He, Roger Barker, Erik Sundström, Gonçalo Castelo-Branco, Patrick Cramer, Igor Adameyko, Sten Linnarsson () and Peter V. Kharchenko ()
Additional contact information
Gioele La Manno: Karolinska Institutet
Ruslan Soldatov: Harvard Medical School
Amit Zeisel: Karolinska Institutet
Emelie Braun: Karolinska Institutet
Hannah Hochgerner: Karolinska Institutet
Viktor Petukhov: Harvard Medical School
Katja Lidschreiber: Karolinska Institutet
Maria E. Kastriti: Karolinska Institutet
Peter Lönnerberg: Karolinska Institutet
Alessandro Furlan: Karolinska Institutet
Jean Fan: Harvard Medical School
Lars E. Borm: Karolinska Institutet
Zehua Liu: Harvard Medical School
David Bruggen: Karolinska Institutet
Jimin Guo: Harvard Medical School
Xiaoling He: University of Cambridge
Roger Barker: University of Cambridge
Erik Sundström: Care Sciences and Society, Karolinska Institutet
Gonçalo Castelo-Branco: Karolinska Institutet
Patrick Cramer: Karolinska Institutet
Igor Adameyko: Karolinska Institutet
Sten Linnarsson: Karolinska Institutet
Peter V. Kharchenko: Harvard Medical School

Nature, 2018, vol. 560, issue 7719, 494-498

Abstract: Abstract RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

Date: 2018
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DOI: 10.1038/s41586-018-0414-6

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